In human-centered AI, UX and software roles are evolving

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Software development has long demanded the skills of two types of experts. There are those interested in how a user interacts with an application. And those who write the code that makes it work. The boundary between the user experience (UX) designer and the software engineer are well established. But the advent of “human-centered artificial intelligence” is challenging traditional design paradigms.

“UX designers use their understanding of human behavior and usability principles to design graphical user interfaces. But AI is changing what interfaces look like and how they operate,” says Hariharan “Hari” Subramonyam, a research professor at the Stanford Graduate School of Education and a faculty fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI).

In a new preprint paper, Subramonyam and three colleagues from the University of Michigan show how this boundary is shifting and have developed recommendations for ways the two can communicate in the age of AI.  They call their recommendations “desirable leaky abstractions.” Leaky abstractions are practical steps and documentation that the two disciplines can use to convey the nitty-gritty “low-level” details of their vision in language the other can understand.

Read the study: Human-AI Guidelines in Practice: The Power of Leaky Abstractions in Cross-Disciplinary Teams

“Using these tools, the disciplines leak key information back and forth across what was once an impermeable boundary,” explains Subramonyam, a former software engineer himself.


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Less is not always more

As an example of the challenges presented by AI, Subramonyam points to facial recognition used to unlock phones. Once, the unlock interface was easy to describe. User swipes. Keypad appears. User enters the passcode. Application authenticates. User gains access to the phone.

With AI-inspired facial recognition, however, UX design begins to go deeper than the interface into the AI itself. Designers must think about things they’ve never had to before, like the training data or the way the algorithm is trained. Designers are finding it hard to understand AI capabilities, to describe how things should work in an ideal world, and to build prototype interfaces. Engineers, in turn, are finding they can no longer build software to exact specifications. For instance, engineers often consider training data as a non-technical specification. That is, training data is someone else’s responsibility.

“Engineers and designers have different priorities and incentives, which creates a lot of friction between the two fields,” Subramonyam says. “Leaky abstractions are helping to ease that friction.”

Radical reinvention

In their research, Subramonyam and colleagues interviewed 21 application design professionals — UX researchers, AI engineers, data scientists, and product managers — across 14 organizations to conceptualize how professional collaborations are evolving to meet the challenges of the age of artificial intelligence.

The researchers lay out a number of leaky abstractions for UX professionals and software engineers to share information. For the UX designers, suggestions include things like the sharing of qualitative codebooks to communicating user needs in the annotation of training data. Designers can also storyboard ideal user interactions and desired AI model behavior. Alternatively, they could record user testing to provide examples of faulty AI behavior to aid iterative interface design. They also suggest that engineers be invited to participate in user testing, a practice not common in traditional software development.

For engineers, the co-authors recommended leaky abstractions, including compiling of computational notebooks of data characteristics, providing visual dashboards that establish AI and end-user performance expectations, creating spreadsheets of AI outputs to aid prototyping and “exposing” the various “knobs” available to designers that they can use to fine-tune algorithm parameters, among others.

The authors’ main recommendation, however, is for these collaborating parties to postpone committing to design specifications as long as possible. The two disciplines must fit together like pieces of a jigsaw puzzle. Fewer complexities mean an easier fit. It takes time to polish those rough edges.

“In software development, there is sometimes a misalignment of needs,” Subramonyam says. “Instead, if I, the engineer, create an initial version of my puzzle piece and you, the UX designer, create yours, we can work together to address misalignment over multiple iterations, before establishing the specifics of the design. Then, only when the pieces finally fit, do we solidify the application specifications at the last moment.”

In all cases, the historic boundary between engineer and designer is the enemy of good human-centered design, Subramonyam says, and leaky abstractions can penetrate that boundary without rewriting the rules altogether.

Andrew Myers is a contributing writer for the Stanford Institute for Human-Centered AI.

This story originally appeared on Copyright 2022


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New Among Us Update Adds Roles, XP, And Lots Of Cosmetics

Starting today, Among Us players can kit out their crewmates and imposters with new cosmetics earned by leveling up or purchased from an in-game store. As part of the Cosmicubes update, the game has received a buffet of new content, including visor cosmetics, new items, two new currencies, a store, and new roles.

Everything from this update starts with the addition of experience points and levels. Players now receive XP for completing tasks, killing crewmates, or correctly guessing who the imposter is. With each level, players get two new currencies: Pods and beans. Beans — along with stars, a premium version of the currency — can be used to purchase items, bundles, and Cosmicubes. Pods, on the other hand, can be used to unlock cube contents, which contain cosmetics and such.

In terms of gameplay changes, players can now play games of Among Us with player roles, changing regular matches in a big way. Crewmates can spawn as Scientists, who can check player vitals at any time, or engineers, who can use vents. Imposters, on the other hand, can become Shapeshifters and take on the appearance of anyone else in the game. Players that are killed can also do more than their remaining tasks as Guardian Angels that can defend players from being killed once.

Along with all of this content, Among Us developer Innersloth included a small teaser in today’s trailer confirming that the studio is currently working on another game. However, no other details on the new title were announced.

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A CIO weighs in on how AI can benefit non-technical roles, particularly HR

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Artificial intelligence is transforming how people work by boosting efficiency and productivity. Human resources departments are using AI to create a more adaptive, flexible, and fluid workplace, one where staffers can develop training, streamline onboarding, identify and evaluate candidates during recruiting, process feedback, respond efficiently to service requests, and manage projects. HR is notoriously manual; information is often kept in silos and answering questions can be a labor-intensive process. Whether it is creating workforce experiences personalized to each employee, or sifting through large amounts of information looking for valuable intelligence, HR professionals benefit by incorporating AI into their processes.

Jeff Gregory, chief information officer of global service provider Thirdera, explained to VentureBeat how AI can impact non-technical roles in an organization, especially in the realm of human resources.

This interview has been edited for clarity and brevity.

VentureBeat: AI makes sense as a tool for data scientists, engineers, and IT pros . How can non-technical roles/organizations, such as sales, marketing, HR, and finance, use it?

Jeff Gregory: It’s a misconception that AI is only a tool for engineers and the like. Sales and HR are two examples of business functions where AI is having a huge impact. In sales, reps in many organizations are using AI to improve forecast accuracy, including the timing, value and likelihood a deal will close. AI is also helping sales determine when and when not to contact customers and prospects, and which pipeline activities to focus on based on the probability of success. This is a huge advantage for time-starved reps, which is basically all of them.

Likewise, AI for HR has the ability to offer an entirely new set of insights and self-service benefits. For example, it can help HR reps understand what truly motivates employees, what creates enthusiasm, what info they need to be successful. AI can also make information easier to find. For example, chatbots can provide instant access to pertinent data on benefits and payroll and offer up suggestions based on past results and insights, allowing HR staff and employees to get answers they need, even if they’re not asking the questions correctly.

VentureBeat: What about HR in particular lends itself to AI? What are some issues HR pros face on a daily basis that would be helped if more HR departments had access to AI technology?

Gregory: HR is the steward of a company. Its reps need to have their pulse on the health and development of employees, and this has everything to do with making sure employees can quickly and easily get answers to questions they have. Presenting the “right” information to employees in a timely fashion is a huge challenge for most organizations. In many cases, employees don’t ask for the right info, leading to follow-up questions and conversations that can delay essential tasks, such as onboarding, training, or benefits. AI and bots provide an incredible opportunity for an organization to get the right information to employees 24/7. Bots also have the ability to “learn” from typical questions and follow-ups, enabling more precise and timely responses. Delivering correct info, links and other resources to employees in an efficient manner can save HR and employees hours of time and improve job satisfaction.

VentureBeat: As a CIO, what are some recommendations you have for HR leaders as they consider if, how, and when to implement AI? Please be specific.

Gregory: The best advice is to start small, then learn and grow. AI is amazing with the insights it can produce. However, it is best to start with a few simple tasks. Take time to fully understand the value and how to utilize it within your organization. A chatbot focused on general HR questions is a great example. This is a learning tool that will provide access to the most important information for the employee. Additionally, this will free the HR team from time-consuming requests, allowing them to focus on other pressing items.


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AI nabs $6.5M to match employees with open roles using AI

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more., a recruitment platform that combines AI with human teams, today announced it has raised $6.5 million in a round led by G20 Ventures. The company, whose latest funding round brings its total to $12 million, says the funds will be used to expand the size of its workforce.

In 2020, talent shortages in the U.S. rose to historic levels, with 69% of employers reporting having difficulty filling jobs, according to a ManPowerGroup survey. This isn’t easily remedied. A report by the Society for Human Resource Management found that filling an open position costs employers an average of $4,129 and takes roughly 42 days.

Fetcher was founded in 2014 as a consumer-centric messaging app called Caliber, which focused on professional networking. After a couple of years running Caliber, Fetcher’s cofounders uncovered an opportunity to automate the functionality they had built into their free consumer app to create a paid software-as-a-service product that helped companies fill roles.

Fetcher’s AI-powered platform provides enterprises with an internal team that trains and monitors data to deliver a pipeline of candidates. After a recruiter uploads a job description and provides some initial feedback, Fetcher’s algorithms look for talent that might match those needs, aggregating candidate information from across the web.

Fetcher offers insights into how many prospects might be needed to ensure a hire and automates outreach accordingly. With the platform, hiring managers can track metrics, including open rates, response rates, and interview booking rates. They’re also able to sync their calendar to create meetings, add scheduling links to email templates, configure availability, and block out times based on a preferred schedule.

“Fetcher … provides the ability to turn on ‘automated sourcing,’ allowing for the platform to run without a recruiter’s intervention. Once the recruiter has trained the AI successfully upfront, automated sourcing simply runs in the background, pushing qualified prospects through an automated email outreach series each day,” a spokesperson told VentureBeat via email. “[This] technology allows recruiting teams to focus more on recruitment marketing and the candidate experience — two pieces of the recruiting funnel that have become ever more important in a competitive job market and with the younger generations of millennials and Gen Z.”

As the pandemic continues, companies are increasingly adopting alternatives to in-person job interviews and talent recruitment. Recruiters PageGroup and Robert Walters last year moved some job interviews and interactions online, following on the heels of tech giants Amazon, Facebook, Google, and Intel. In spite of fears that these tools might exhibit biases against certain groups of candidates, at least a few businesses have begun piloting candidate screenings that ostensibly help recruiters become more efficient, cut down on recruitment costs, and boost overall candidate satisfaction.

Fetcher’s competitors include Plum, which asks job candidates to fill out problem-solving and personality tests that award points for “talents” like adaptation, communication, inclusion, and innovation. Another rival, Vervoe, offers AI tools that test would-be employees’ on-the-job skills with a mix of general assessments, coding challenges, and personality quizzes. There’s also Headstart, which recently raised $7 million for AI that can mitigate recruitment bias;  Xor, a startup developing an AI chatbot platform for recruiters and job seekers; and Phenom People, a human resources platform that taps AI to help companies attract new talent.

Fetcher claims its AI technologies work to decrease unconscious bias while increasing the size and scope of diverse talent pools. Since May 2020, the New York-based startup has partnered with over 150 companies, including Peleton, Behr, and Velcro, and seen revenues increase 10 times, with annual recurring revenue more than doubling in the last nine months alone.

“Fetcher provides diversity metrics, conversion metrics, and more all within the platform. This allows teams to benchmark each role versus the full company, benchmark each recruiter against other team members, and benchmark the company’s metrics against the industry standards,” a spokesperson told VentureBeat via email. “All of these real-time metrics ensure that recruiters are meeting their goals, and if they are not, gives them a clear path as to what levers might need to change in order to get there. Overall, Fetcher provides predictive modeling for pipeline building so that companies can always be ahead of the game when hiring in that department becomes a top priority.”

Alongside G20 Ventures, KFund and returning investors Slow and Accomplice participated in Fetcher’s round announced today. Fetcher has 80 employees.


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